Future projection of greenhouse gas emissions due to permafrost degradation using a simple numerical scheme with a global land surface model

• Published in: Progress in Earth and Planetary Science
• Main Authors: Tokuta Yokohata, Kazuyuki Saito, Akihiko Ito, Hiroshi Ohno, Katsumasa Tanaka, Tomohiro Hajima, Go Iwahana
• Format: Article in Journal/Newspaper
• Language: English
• Published: SpringerOpen 2020
• Subjects: Permafrost degradation; Carbon cycle feedback; Climate change; Geography. Anthropology. Recreation; G; Geology; QE1-996.5; Arctic; Ice; permafrost
• Online Access: https://doi.org/10.1186/s40645-020-00366-8; https://doaj.org/article/20f5dc0c6b504f8e902225fc8719ae8f

Abstract The Yedoma layer, a permafrost layer containing a massive amount of underground ice in the Arctic regions, is reported to be rapidly thawing. In this study, we develop the Permafrost Degradation and Greenhouse gasses Emission Model (PDGEM), which describes the thawing of the Arctic permafrost including the Yedoma layer due to climate change and the greenhouse gas (GHG) emissions. The PDGEM includes the processes by which high-concentration GHGs (CO2 and CH4) contained in the pores of the Yedoma layer are released directly by dynamic degradation, as well as the processes by which GHGs are released by the decomposition of organic matter in the Yedoma layer and other permafrost. Our model simulations show that the total GHG emissions from permafrost degradation in the RCP8.5 scenario was estimated to be 31-63 PgC for CO2 and 1261-2821 TgCH4 for CH4 (68th percentile of the perturbed model simulations, corresponding to a global average surface air temperature change of 0.05–0.11 °C), and 14-28 PgC for CO2 and 618-1341 TgCH4 for CH4 (0.03–0.07 °C) in the RCP2.6 scenario. GHG emissions resulting from the dynamic degradation of the Yedoma layer were estimated to be less than 1% of the total emissions from the permafrost in both scenarios, possibly because of the small area ratio of the Yedoma layer. An advantage of PDGEM is that geographical distributions of GHG emissions can be estimated by combining a state-of-the-art land surface model featuring detailed physical processes with a GHG release model using a simple scheme, enabling us to consider a broad range of uncertainty regarding model parameters. In regions with large GHG emissions due to permafrost thawing, it may be possible to help reduce GHG emissions by taking measures such as restraining land development.